关键词: 轮毂焊缝, 目标检测, Retinanet, CoTNet, Transformer

Abstract: This paper proposes a real-time detection and positioning system for hub weld based on deep learning method, designs a visual inspection hardware platform for hub weld, describes the principle of the multi-specification hub weld detection and location algorithm, describes the principle of the object detection algorithm Retinanet based on convolutional neural network and the object detection algorithm CoTNet based on Transformer architecture, optimizes Cot structure and proposes Cotx structure, so that easily replaces the general convolution layer in convolution neural network. Under the Pytorch framework, this paper simplifies the Retinanet network, and optimizes the detection performance of Retinanet network on the hub weld dataset through the fusion and comparison experiment of Cotx structure and Retinanet network. Experimental results show that better detection effets can be obtained by replacing the last few feature extraction layers of Retinanet with Cotx structure. At the production site, the online real-time detection of hub weld is carried out for 30 days, with an average detection accuracy of 99.7% and a single detection time of 7ms, which can meet the requirements of the enterprise production.

Key words: hub weld, object detection, Retinanet, CoTNet, Transformer